Federated AI and Cloud Native Frameworks for Secure Digital Transformation and Real-Time Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2024.0702008Keywords:
Federated AI, Federated Learning, Cloud-Native Architecture, Real-Time Analytics, Digital Transformation, Zero-Trust Security, Microservices, Kubernetes, Data Privacy, Distributed IntelligenceAbstract
The rapid evolution of digital ecosystems has accelerated the adoption of cloud-native architectures and artificial intelligence (AI) to enable scalable, real-time analytics across distributed environments. However, increasing concerns regarding data privacy, regulatory compliance, and cybersecurity threats have necessitated innovative approaches to secure digital transformation. Federated Artificial Intelligence (Federated AI) has emerged as a promising paradigm that enables collaborative model training across decentralized data sources without transferring sensitive data to centralized repositories. This paper explores the integration of Federated AI within cloud-native frameworks to enhance secure digital transformation and real-time analytics. It proposes an architectural model that combines containerized microservices, Kubernetes orchestration, zero-trust security principles, and federated learning mechanisms to ensure data confidentiality, system scalability, and regulatory compliance. The study outlines a structured methodology for implementing federated AI pipelines in hybrid and multi-cloud environments while maintaining high availability and low latency. The analysis highlights the strategic benefits, architectural considerations, and operational challenges associated with federated AI adoption. The findings demonstrate that combining federated intelligence with cloud-native infrastructures enables organizations to achieve secure, scalable, and resilient real-time analytics without compromising data privacy or governance standards
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